Is it possible to apply the associative property of the convolution operation when it is followed by a non-linearity?
Yes, when you have non-linearity it is not possible to combine your convolution steps.
However, you can approximate the two layers network with one layer net according to the Universal Approximation Theorem. You will probably need to use something like a knowledge distillation technique to do it. Note that, the theorem doesn't say anything about the number of neurons required or if the learning techniques that we usually use will work well.
Also, $ReLU(x)$ is a linear mapping when $x \geq 0$, so if your input, weight, and biases are $\geq 0$, the net can be exactly modeled with a single layer network.